Time Course of Brain Activity during Change Blindness and ... - unige

Time Course of Brain Activity during Change Blindness and Change Awareness: Performance is Predicted by Neural Events before Change Onset Gilles Pourtois1,2, Michael De Pretto1,3, Claude-Alain Hauert3, and Patrik Vuilleumier1,2,3

Abstract & People often remain ‘‘blind’’ to visual changes occurring during a brief interruption of the display. The processing stages responsible for such failure remain unresolved. We used eventrelated potentials to determine the time course of brain activity during conscious change detection versus change blindness. Participants saw two successive visual displays, each with two faces, and reported whether one of the faces changed between the first and second displays. Relative to blindness, change detection was associated with a distinct pattern of neural activity at several successive processing stages, including an enhanced occipital P1 response and a sustained frontal activity (CNV-like

INTRODUCTION The ability to detect changes in the environment is a crucial aspect of perception and is undeniably important in our everyday life. However, change detection is not a simple and straightforward process as it might first appear. Behavioral studies show that large visual changes may occur directly in our field of view but fail to enter awareness, even when such changes are significant, repeatedly made, and anticipated (Rensink, 2002). This phenomenon of ‘‘change blindness’’ is not caused by intrinsic properties of visual stimuli making changes difficult to notice, because the same changes are readily detected once attention is drawn to them (Simons & Rensink, 2005). Change blindness may arise for simple geometric shapes or colors (Simons, 2000), complex scenes (Grimes, 1996), or familiar objects such as faces (Ro, Russell, & Lavie, 2001). Such apparent blindness for otherwise salient visual stimuli can serve as the flip side of processes involved in conscious change detection and thus provide important insights on the functional constraints of perceptual awareness.

1

Neurology & Imaging of Cognition Laboratory, Department of Neuroscience & Neurology Clinic, 2Swiss Center for Affective Sciences, 3University of Geneva

D 2006 Massachusetts Institute of Technology

potential) after the first display, before the change itself. The amplitude of the N170 and P3 responses after the second visual display were also modulated by awareness of the face change. Furthermore, a unique topography of event-related potential activity was observed during correct change and correct nochange reports, but not during blindness, with a recurrent time course in the stimulus sequence and simultaneous sources in the parietal and temporo-occipital cortex. These results indicate that awareness of visual changes may depend on the attentional state subserved by coordinated neural activity in a distributed network, before the onset of the change itself. &

Several possible mechanisms may be responsible for change blindness. A key aspect is thought to involve a limited processing capacity of the brain, resulting in the inability to retain an exhaustive representation of sensory information over time and/or across brief interruptions in inputs (e.g., Simons & Rensink, 2005; Noe¨, Pessoa, & Thompson, 2000). However, different mechanisms might potentially operate at distinct stages of processing and induce change blindness in different ways. According to Simons (2000), an inability to detect changes between two successive stimuli might arise because of a failure in processing the first stimulus, the second stimulus, both, or some links between these two stages. For instance, a new stimulus might replace and erase the trace of the previous stimulus, precluding the detection of any change unless attention is directed to its location at the time when it occurs (Shapiro, Arnell, & Raymond, 1997). Alternatively, only the first stimulus might be fully retained, and no information might be extracted from a second stimulus in the absence of indirect cues signaling the possibility of change (e.g., apparent motion; see Simons, 2000). Another hypothesis is that sensory information from each stimulus is fully processed, but none of them is stored beyond its presentation (Noe¨ et al., 2000). Still, other possibilities are that each successive stimulus can be

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perceptually processed and stored while only their comparison fails (Mitroff, Simons, & Levin, 2004; ScottBrown, Baker, & Orbach, 2000) because of some insufficiency in internal representation or short-term memory capacity or that information from two successive stimuli is merged into a single representation, perhaps because conscious scene perception builds slowly over time (Simons, 2000). Behavioral studies have clearly shown that focused attention is necessary to detect changes, because attended and task-relevant objects in a scene are more likely to be encoded and compared (Rensink, O’Regan, & Clark, 1997; Shapiro et al., 1997), but attention at the time of change onset might not be sufficient (Simons & Rensink, 2005) because changes to attended objects sometimes go unnoticed when these changes are unexpected (Triesch, Ballard, Hayhoe, & Sullivan, 2003; Williams & Simons, 2000). A useful approach to investigate some of these different possible mechanisms of change blindness, each potentially involving different stages of processing during scene perception, can be provided by event-related potentials (ERPs). By indexing the time course of brain activity both before and after change onset, ERPs might help determine whether attention is critically required at the time of stimulus change only, or whether other processing stages are differentially modulated. Few studies have investigated the neural correlates of change blindness and change awareness, despite much recent interest in the mechanisms of conscious vision (Koch, 2004) and important theoretical implications for understanding visual perception (Block, 2005). In a pioneer study (Beck, Rees, Frith, & Lavie, 2001), functional magnetic resonance imaging (fMRI) was used during a simplified ‘‘f licker’’ paradigm, in which a change between two successive visual displays could involve either a face or a house (or none). Correct detection of changes (as compared to change blindness) was associated with enhanced activation of category-specific regions in the ventral temporal cortex (e.g., in fusiform gyrus for face changes), together with enhanced activation of more dorsal regions in the frontal and parietal cortex (see also Pessoa & Ungerleider, 2004). However, fMRI relies on slow hemodynamic responses and cannot reveal the precise temporal dynamics of activity associated with blindness or awareness of changes over the successive stages of processing for each visual event. In the study of Beck et al. (2001), the critical enhancement of visual and fronto-parietal areas could potentially follow, co-occur, or even precede the actual sensory inputs into the visual system. Indeed, other neuroimaging data (Corbetta, Kincade, Ollinger, McAvoy, & Shulman, 2000; Kastner, Pinsk, De Weerd, Desimone, & Ungerleider, 1999) suggest an important role of preparatory states and shifts of baseline activity during selective attention, preceding any stimulus-driven responses (see Tallon-Baudry, Bertrand, Henaff, Isnard, & Fischer, 2005; Super, van der Togt, Spekreijse, &

Lamme, 2003). Thus, it is possible that some critical differences in neural activity during change awareness relative to change blindness might already arise before the activity elicited by the visual change itself. Because of this slow temporal resolution, previous fMRI studies could not answer the question of whether change blindness (e.g., in a flicker paradigm) primarily results from a nonexhaustive processing of the initial stimulus, of the subsequent changing stimulus, of both and/or from a deficiency in the link between stimuli allowing their comparison (see Simons, 2000). Here we used ERPs in a simplified flicker paradigm adapted from Beck et al. (2001), allowing to track brain activity on a millisecond basis evoked by each successive stimulus in conditions of either change blindness or change awareness. We used faces as visual stimuli because they evoke a specific component in ERPs (N170; Bentin, Allison, Puce, Perez, & McCarthy, 1996) in addition to other well-documented exogenous responses. Our main goal was to determine whether any differential activation associated with change blindness versus change awareness might arise during responses to the changing stimulus and/or already before the change itself (see Vogel & Machizawa, 2004, for similar approach in a working-memory task). We could also test whether any difference between change blindness and awareness might concern the early stages of visual processing (e.g., P1–N1), stimulus categorization stages (e.g., N170), and/ or later comparison and decision stages (e.g., P3). Only a few previous studies have used electroencephalogram (EEG) to investigate change blindness, but all concentrated on responses consecutive to the change, using either flicker paradigms with successive geometric shapes (Koivisto & Revonsuo, 2003; Niedeggen, Wichmann, & Stoerig, 2001) or a cyclic presentation of alternating stimuli (Fernandez-Duque, Grossi, Thornton, & Neville, 2003). Comparing awareness versus blindness for visual changes in these studies has shown differential activity in a posterior negative wave approximately 200 msec postchange (Koivisto & Revonsuo, 2003), as well as later increases in the amodal P3 component (Niedeggen et al., 2001), typically associated with conscious decision stages (Polich & Kok, 1995). Earlier effects over the frontal and occipital regions were also found to be approximately 100 msec after the onset of a reported change during a cyclic alternation of stimuli (Fernandez-Duque et al., 2003). These results suggest a complex sequence of processing stages during conscious change detection but do not address the question of whether any critical difference in brain activity before change onset might influence the ability to detect such change at a later point in time, as specifically investigated here. Furthermore, previous ERP studies of change blindness concentrated on waveform analysis (i.e., amplitude and latency of selected components; see Picton et al., 2000), whereas we complemented this approach with topographic and source localization

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methods that provide additional insights on the spatiotemporal dynamics of brain activity (Michel, Seeck, & Landis, 1999; Lehmann & Skrandies, 1980). In the present study, participants performed a change detection task adapted from Beck et al. (2001), where each trial consisted of a sequence of two successive displays, each with a pair of two different faces presented in the peripheral visual field (Figure 1). The first pair of faces (stimulus S1) was shown for 150 msec, one in each hemifield, followed by an empty screen for 150 msec, and then replaced by a second pair of faces (stimulus S2) for another 150 msec. Critically, in the second pair, one of the two faces could be different than in the first pair, either on the right or left side (a third of trials each), or the two faces could be the same as in the first pair (a third of trials). Participants reported whether they had detected a changing face or not (without any speed constraints). To ensure an adequate central fixation, participants also had to monitor whether numbers or letters were presented at the center of the screen (see also Beck et al., 2001). Crucially, we could compare trials with similar stimuli but two different perceptual outcomes, that is, when a face identity change was either correctly perceived (change awareness) or missed (change blindness) and trials with different stimuli but similar perceptual outcome (i.e., when a face identity change was missed, relative to an absence of change correctly reported). By recording EEG over the two successive stimuli in this simplified flicker paradigm and combining both waveform and topographical analyses of ERPs, we might directly find evidence for the different theoretical hypotheses put forward by Simons (2000). If change blindness results from a rapid fading of the trace (or

deficient initial encoding) of the first stimulus (S1), conditions may mainly differ during the exogenous visual (e.g., P1–N1) and categorical (N170) responses to S1 (i.e., before change itself ), whereas only ERPs to S2 might differ between conditions if processing of the second stimulus is more determinant. Alternatively, early visual responses to both the first and second stimuli might be unaffected if change blindness results from some failure at postperceptual comparison stages or in working memory. In addition, topographic analyses and source localization enabled us to determine whether awareness of changes might correlate with a unique configuration of brain generators at a particular latency or rather with a prolongation or a higher strength of one (or more) configuration(s) similar to those present during change blindness.

METHODS Participants Participants in the main EEG experiment were 12 righthanded naive students (6 women; mean age = 24 years, SD = 3 years) from the University of Geneva. We also recruited a different group of 18 other students, 10 of whom (6 women; mean age = 25 years, SD = 3 years) participated in a behavioral pilot test to define appropriate stimulus parameters to obtain an equal proportion of correct detections and misses and 8 of whom (4 women; mean age = 26 years, SD = 3 years) participated in another control behavioral test to establish that changes in a single visual stimulus could be correctly detected when presented alone and fully attended (see below). All participants had normal or

Figure 1. Sequence of events within a trial during our EEG experiment. Two visual displays (each consisting of a small alphanumerical symbol presented centrally, plus two different faces in the periphery) were briefly presented for 150 msec each, separated by a 150-msec empty screen (here with a left face change). Subjects were asked to report whether a number (1–9) appeared at the central location task (to ensure adequate central fixation) and then whether one of the face identities has changed (using two successive response screens, not shown here).

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Journal of Cognitive Neuroscience

Volume 18, Number 12

corrected-to-normal vision and no history of neurological or psychiatric illness.

Visual Stimuli During the critical EEG experiment, each visual display consisted of a character presented at the center of the screen (either a letter [from a choice of nine: A, E, H, K, N, R, T, U, X] or an arabic number [from 1 to 9]), together with two flanking faces (each 78 88, with their center 5.78 to the right or left of the central character), all on a black background (Figure 1). The central stimuli, as well as the size and position of peripheral faces, were preselected to adjust task difficulty during a behavioral pretest. Face images were grayscale photographs taken from a standardized set (KDEF; ¨ hman, Department of D. Lundqvist, A. Flykt, and A. O Neurosciences, Karolinska Hospital, Stockholm, Sweden, 1998), all with a neutral expression (50 different identities, half of each sex). The two faces shown on any given trial were always from two different person identities, but with the same sex. Each individual face was combined with one out of two other faces, with similar external hair shape to minimize any strategy based on low-level visual cues, yielding 100 different pairs in total (with each AB combination mirrored by a symmetric BA pairing).

Procedure in the Main Electroencephalogram Experiment On each trial, the two displays (S1 and S2) were presented in rapid succession after a central fixation cross (250 msec) and a fixed empty delay (250 msec) (Figure 1). Each display was presented for 150 msec, separated by an empty screen for 150 msec. In S2, the two faces could be identical to those in S1 (one third of trials, no-change condition) or one of the two faces (either on the left or right side) was replaced by a new identity, always with the same sex (two thirds of trials, half on the left and half on the right). The short stimulus duration (150 msec) plus the central task ensured that peripheral change detection was performed without saccades (similar to Beck et al., 2001). Orthogonally to the peripheral face conditions, the central character was also systematically manipulated between S1 and S2, such that either two different letters were successively presented (half of trials) or a number was presented in one the two displays (first or second, a quarter of trials each). Correct performance in the central task was likely to require adequate fixation, because the characters were too small and similar to be easily discriminated in peripheral vision, as confirmed during pilot testing and by monitoring eye position by a closed TV circuit during EEG recordings. Our procedure thus ensured that the occurrence of a central number was unpredictable and

that fixation remained central across all face conditions. Note that small changes in the central alphanumerical symbol between S1 and S2 were constant across the different experimental conditions and therefore canceled out any potential contamination of the ERPs to peripheral face stimuli. Participants had to perform a dual task: (1) to monitor the occurrence of a number at the center and (2) to report any change in the identity of one of the two peripheral faces. Responses were always required according to this task order (Figure 1): 600 msec after onset of S2, a response screen prompted participants to report whether a number had been presented or not (by pressing one of two buttons, within a 2-second limit); then a second response screen prompted them to report whether one of the faces had changed or not (again using two buttons, within a 2-second limit). Instructions emphasized accuracy in the primary central task and also urged participants against guessing by asking them to adopt a strict criterion for their ‘‘change’’ responses (i.e., report a face change ‘‘only when seen with confidence,’’ otherwise report no change). All stimuli were presented on a 17-in. computer screen with a PC Pentium II running STIM software. Participants were seated in a shielded room in front of the screen (viewing distance = 50 cm). Behavior was monitored by closed-circuit TV. Subjects received one practice block of 30 trials, followed by 10 runs of 60 trials each (600 trials total), with a different random order of the different experimental conditions (20 trials with no face change, 20 with a left face change, and 20 with a right face change). Each face pair (n = 100) appeared four times in each condition. Thus, in total, each face identity (n = 50) was presented 48 times, with equal probability in the left or right visual field (LVF and RVF, respectively). Each identity appeared equally often in the first than in the second display on trials with a change and also equally often on trials with no change. Behavioral Pretest Before the EEG experiment, we performed a pilot behavioral study in a different group of participants (n = 10) to determine the stimulus parameters and procedure that fulfilled the following criteria: (a) a balanced distribution between the rate of change blindness and change awareness trials (approximately half each), allowing an adequate comparison of each condition; (b) a low rate of false detection on trials with no change (20% or lower), to ensure a clear difference in perceptual experience during change awareness versus blindness; (c) a high accuracy in the central task, but without ceiling performance and without any tradeoff with peripheral face detection. Using the same stimulus parameters as above, this behavioral pretest showed an equal numbers of misses (mean 49%) and hits (mean 51%) on trials with a peripheral face change, whereas

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there was a low rate of false alarms (17%) on trials with no face change, and also a good performance in the central number task (2.9% of errors).

Control Behavioral Experiment In addition, we tested another group of participants (n = 8) on a much more simple task, in which faces were presented unilaterally in the periphery, always in LVF or always in RVF (for separate blocks of 100 consecutive trials on each side), while subjects maintained fixation at the screen center. Like in the main experiment, the first face (150 msec) was followed by a brief blank interval (150 msec) and then a second face (150 msec) that could be either the same or different (half of trials each). But here, changes occurred at a fully predictable and attended position in the peripheral visual field, without any other concurrent task. In these conditions, face changes were correctly detected in a large majority of trials (90% hits) in both visual fields (but with a significant advantage for the LVF relative to the RVF: 91% vs. 87% correct, respectively, t(7) = 2.3, p = .055, consistent with a right hemispheric dominance in face processing; see also below). False alarm rates were low (6% in LVF and 9% in RVF, t(7) = 1.24, ns). This control experiment shows that face identity changes could be reliably perceived when the location of changes could be predicted and attended, indicating that failures in the main EEG experiment were not because of the perceptual difficulty of face identification, but to higher attentional demands of the task, and thus reflected a genuine ‘‘change blindness’’ phenomenon (Simons & Rensink, 2005).

Data Acquisition Visual ERPs were recorded and processed using a NEUROSCAN 64 channels (Synamps, El Paso, TX). Horizontal and vertical electrooculograms (EOGs) were monitored using four facial bipolar electrodes placed on the outer canthi of each eye and in the inferior and superior areas of the left orbit. The 62 Ag/AgCl electrodes were mounted on a quickcap, according to the extended 10-20 system, with a linked-mastoid reference, amplified with a gain of 30 K and bandpass filtered at 0.01–100 Hz with a 50-Hz notch filter. Impedance was kept below 5 k . EEG and EOG were continuously acquired at a rate of 500 Hz and stored for off-line averaging. EEG was corrected for eye blinks (Gratton, Coles, & Donchin, 1983). After removal of artifacts (epochs with EEG or residual EOG exceeding ±75 AV), epoching was made from 200 msec before the onset of S1 until 800 msec after onset, covering the complete stimulus sequence. EEG data were first baseline-corrected on the prestimulus interval (200/0 msec), averaged to individual ERPs, and finally low-pass filtered at 30 Hz. For topographic

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analyses and source reconstructions, averaged ERPs were re-referenced using a common average reference.

Data Analysis Global Waveform Modulations To determine the timing of differences between conditions over the whole stimulus sequence (e.g., change detection vs. change blindness), we calculated pointwise paired t tests between ERPs in each condition, using the mean raw amplitude recorded every 2 msec in each subject and the variance across subjects. The first time point where t test values exceeded the .05 alpha probability criterion for at least 10 consecutive data points (>20 msec at 500 Hz) over at least two adjacent electrodes was considered as the onset of a significant difference (see Guthrie & Buchwald, 1991). Although this approach does not entirely protect against spurious significant effects because consecutive EEG samples are not independent, this combined spatio-temporal criterion provides a reliable global estimate of the onset and offset of stable ERP effects (in time and space), without any a priori selection of restricted time points or limited electrodes (see Murray et al., 2004; Guthrie & Buchwald, 1991, for similar methods). Component Analyses Amplitudes of prominent ERP components were quantified in terms of mean voltage within a specified latency window (centered on the component’s peak), with respect to a 200-msec prestimulus baseline (Picton et al., 2000). Latency analyses of the components were also performed but not reported in details because they did not vary significantly as a function of our experimental factors (see Table 1). Relevant sites for each component were selected based on previous studies (e.g., Fernandez-Duque et al., 2003; Koivisto & Revonsuo, 2003) and topographic properties of the current data set. After the onset of S1, three conspicuous components were identified (Table 1 and Figure 2A): a lateral occipital P1 (Luck, Heinze, Mangun, & Hillyard, 1990), a posterior temporal N170 (Itier & Taylor, 2004a; Bentin et al., 1996), and an occipital positivity corresponding to P2 (Noesselt et al., 2002; Johannes, Munte, Heinze, & Mangun, 1995). P1 (80–140 msec) was maximal at lateral and superior occipital electrodes O1/PO7/PO5/PO3 (left hemisphere) and O2/PO8/PO6/PO4 (right), with a typical dipolar topography (Luck et al., 1990). The N170 (130–200 msec) had a more anterior occipito-temporal topography (PO7/PO5/P7/P5 over the left, PO8/PO6/P8/ P6 over the right), with inverted polarity relative to P1 (Bentin et al., 1996; George, Evans, Fiori, Davidoff, & Renault, 1996; Jeffreys & Tukmachi, 1992). The P2 (210– 310 msec) was maximal at posterior medial electrodes (PO3/POz/PO4). A negative activity was also present


Table 1. Mean Amplitude (in AV) and Mean Latency (in msec) of the Different ERP Components Left Face Change Visual Display First (S1)

Component P1

Undetected

p Value Detected vs. Undetected (Amplitude)

No Change

p Value Detected vs. No Change (Amplitude)

7.3 (110.0)

6.6 (110.4)

.04

6.8 (109.0)

.07

5.9 (168.7)

6.1 (169.2)

.75

6.2 (168.5)

.45

4.3 (240.3)

3.9 (237.9)

.52

3.7 (240.4)

.22

0.7 (289.6)

1.9 (293.7)

.01

1.1 (295.0)

.41

4.4 (407.9)

3.9 (408.4)

.49

3.9 (407.2)

.36

2.8 (454.5)

4.1 (454.7)

.04

3.4 (454.3)

.18

P2

5.3 (501.0)

5.2 (504.3)

.85

5.1 (504.3)

.57

P300

9.4 (708.8)

7.1 (697.1)

.002

7.0 (697.1)

.001

N170 P2 Frontal Second (S2)

Detected

P1 N170

250 msec after S1 (50 msec before the onset of the second visual display), with a fronto-central topography and amplitude variation at electrodes AF4/F8/F6 (right) and AF3/F7/F5 (left) sharing some electrophysiological similarities with the early phase of a CNV potential (Walter, Cooper, Aldridge, McCallum, & Winter, 1964). For ERPs after S2, the same three visual peaks were identified, including a P1 (370–440 msec postonset of S1), N170 (430–500 msec), and P2 (450–530 msec). The first two components had a similar topography during S2 than during S1 (Figure 7), but P2 had a slightly different topography, now maximal at centro-parietal (C1/Cz/C2, CP1/CPz/CP2, and P1/Pz/P2) rather than occipito-parietal electrodes. In addition, at a later latency (640–750 msec poststimulus), a large and sustained positivity was apparent, with a centro-parietal topography and maximal amplitude at C1/Cz/C2, CP1/CPz/CP2, and P1/Pz/P2, compatible with a P3 or P300 (Polich & Kok, 1995). Differences between conditions were analyzed by comparing the mean amplitude of each component at adjacent electrode positions over each hemisphere (Picton et al., 2000). Repeated measures analyses of variance (ANOVAs) were performed using a Greenhouse–Geisser correction for nonsphericity when required, with three main experimental conditions: face-change correctly reported (change awareness), face-change missed (change blindness), and no-change correctly reported. Topographic Analyses To identify ERP differences due to variations in global topography, rather than just local amplitude effects at selected electrodes, we used a temporal segmentation algorithm derived from standard spatial cluster analysis (Pascual-Marqui, Michel, & Lehmann, 1995), as already applied in other cognitive domains (Itier & Taylor, 2004a; Leonards, Palix, Michel, & Ibanez, 2003; Michel et al., 1999). This procedure determines the dominant topographies appearing in the group-averaged ERPs

over time, across all conditions. Because only topographic landscape differences are of interest, scalp maps are first scaled to unitary strength by dividing the voltage at each electrode by the global field power (GFP). The optimal number of topographic maps explaining the whole data set is determined objectively by a cross-validation criterion (Pascual-Marqui et al., 1995). The dominant topographies (identified in the groupaveraged data) are then fitted to ERPs of each individual subject using a spatial fitting procedure, providing a quantitative estimates of their relative expression (in strength and duration) across subjects and conditions (Michel et al., 1999). These analyses were carried out using CARTOOL software (version 3.0; developed by D. Brunet, Functional Brain Mapping Laboratory, Geneva, Switzerland). Distributed Source Localization To estimate the likely neural sources underlying the electric field configurations identified by the segmentation analysis, we used a distributed linear inverse solution, based on a local auto-regressive average (LAURA) model for the unknown current density, derived from biophysical laws describing electric fields in the brain (Grave de Peralta Menendez, Murray, Michel, Martuzzi, & Gonzalez Andino, 2004). This source localization technique provides a linear distributed inverse solution, using a realistic head model with 4024 lead field nodes, selected from a 6 6 6-mm grid equally distributed within the gray matter of the Montreal Neurological Institute template brain. This method emulates the properties of brain activity by computing multiple simultaneously active sources based on biophysically driven inverse solutions without a priori assumption on the number and position of the possible generators. The procedure was implemented using CARTOOL software. Additional analyses were also performed using a similar source estimation procedure by

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Figure 2. ERP data for the whole stimulus sequence. (A) Group-averaged (n = 11) waveforms (in the left face change detection condition) are shown superimposed across all electrodes (n = 62), revealing a series of conspicuous electric deflections (P1, N170, P2, sustained negative activity, and P3) after the first visual display (S1) and the second visual display (S2). (B) Modulations of ERP waveform during change detection versus change blindness were assessed by a running paired t test for each electrode ( y axis) and each time point (x axis) using the variance across subjects. Statistical p values are coded on a three-level grayscale (see Methods for details). This global comparison indicates four distinct periods where the two conditions differed (for >20 msec), each highlighted by dotted outline boxes. The earliest effect occurred at approximately 50 to 120 msec (1), with subsequent effects at 290 to 375 msec (2), 400 to 450 msec (3), and 600 to 800 msec (4).

low-resolution electromagnetic tomography (LORETA, version 3; Pascual-Marqui, Michel, & Lehmann, 1994).

RESULTS Behavioral Results for the Central Fixation Task During EEG recordings, all 12 participants performed accurately on the central number task (mean error rate = 2.3%), except for one subject who was clearly deviant (12% errors) relative to the group mean ± 2 SD. This subject was excluded from subsequent analyses. For the remaining participants, the mean error rate in the central task dropped to 1.4% (min = 0, max = 3.3%).

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These behavioral data ensure that participants correctly maintained fixation at the center. Importantly, error rates in this central task did not vary (all t < 1.3, p > .20) as a function of whether subjects detected the peripheral face change (24.4% of trials with central errors), failed to detect this change (38.9%), or correctly reported the absence of a change (30.0%). These behavioral results were corroborated by eye movements recorded during EEG. For each condition (detected, missed, or no change), we calculated the average eye position measured by the horizontal EOG during three successive bins of 150 msec, reflecting the occurrence of any saccade during S1 (150 msec), S2 (150 msec), or the blank interval (150 msec). A


3 (conditions) 3 (bins) ANOVA on these data did not reveal any significant effect or interaction (all F < 1, p > .34). Altogether, these data indicate that the peripheral change detection task was performed without any systematic horizontal eye movement or any tradeoff with the central task performance. Behavioral Results for the Peripheral Face Task Overall, our paradigm was successful to achieve a balanced proportion of trials where a face change was either correctly detected or missed (mean 43% vs. 57%, respectively), whereas false alarms remained relatively rare when no change occurred (9.4%). Thus, participants reported a face change only when it was perceived with sufficient confidence, ensuring that these trials were associated with robust awareness of the new face as compared with change blindness and no change. Moreover, we observed a strong asymmetry between the two hemifields. Participants correctly reported 61% of changes in LVF (range = 21%–89%) but only 25% of changes in RVF (range = 7%–44%). This asymmetry, observed for 10 out of 11 subjects, was highly significant [t(10) = 5.6, p < .001] and reflected a true difference in perceptual sensitivity, as shown by a higher discrimination measure (d0) for changes in left than right faces [mean = 2.17 vs. 1.14; t(10) = 5.18, p < .001]. This is consistent with neuropsychological data showing a dominance of the right hemisphere/LVF for face processing (Grusser & Landis, 1991). An item-based analysis indicated that hits (correct change detection) were randomly distributed across the 100 different face pairs and did not concern a specific subset of stimuli. Event-related Potential Results EEG recorded from the onset of S1 to S2 allowed us to determine the processing stage(s) when neural activity differed among three conditions: (a) face-change correctly reported, (2) face-change missed, and (3) nochange correctly reported. Thus, the critical comparison between awareness versus blindness [condition (a) vs. (b)] concerned ERPs for the same stimulus condition but with different percepts. ERPs were computed using only those trials (97.7%) where performance on the central task was correct and blocks when performance on the peripheral face task was associated with a low rate of false alarms (10 consecutive time points (i.e., >20 msec; see Guthrie & Buchwald, 1991). The earliest difference (Figure 2B) started at approximately 50–120 msec (i.e., during S1). Other differences occurred around approximately 290–375 msec and approximately 400–450 msec, as well as later, approximately 600–800 msec post-S1 onset. These ERP modulations suggest that conscious awareness of face changes corresponds to a distinct pattern of brain responses at several successive stages of processing. More critically, brain activity corresponding to successful change detection already differed from brain activity corresponding to change blindness performance during two distinct time periods before the occurrence of the change itself, that is, during S1 and during the interval just preceding S2 (Figure 2B). However, these data alone do not indicate whether the differences between conditions concerned the timing, amplitude, and/or topography of activity (see below). Using a similar pointwise t test, we also compared trials with correct change detection versus no change and trials with change blindness versus no change. In the former comparison, two ERP modulations similar to the above were identified (around 400–450 and 600– 800 msec), but the latter comparison did not show any reliable effect across time points and electrodes. This negative result indicated that despite the different visual input in these two conditions (change blindness vs. no change), both subjects’ awareness and brain activity similarly categorized the stimuli as an absence of change.

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These data therefore reveal distinctive ERPs for consciously perceived changes of faces, but provide no evidence for an effect of missed changes. Classical Component Analyses A sequence of well-known exogenous responses was identified, including the occipital P1 (Luck et al., 1990), posterior temporal N170 (Bentin et al., 1996), and parietooccipital P2 (Noesselt et al., 2002; Johannes et al., 1995), for each of the two successive face displays (S1 and S2). These visual responses were followed by a late P3 component, arising after S2, before the participants’ response (see Figure 2A). The amplitude and latency of each peak (Picton et al., 2000) were examined by repeated measures ANOVAs, using the factors of Perceptual Condition (change awareness, change blindness, and no-change correct), Hemisphere (right and left),

and the relevant Electrode Sites (e.g., anterior vs. posterior position, lateral vs. central line, where appropriate; see Methods). Only results for amplitude differences are reported, because the same analyses on latencies did not show any significant effect (all p > .10; see Table 1). These analyses confirmed that brain activity corresponding to change awareness differed from brain activity corresponding to change blindness already approximately 200 msec before the change actually occurred. The amplitude of P1 evoked by S1 was enhanced when a subsequent face change was detected versus missed (Figure 3 and Table 1). A 3 (condition) 2 (hemisphere) 4 (electrode) ANOVA on mean amplitude of this component revealed a significant main effect of condition [F(2,20) = 3.66, p = .046]. This first P1 was larger for change detection (mean = 7.31 AV) than change blindness (mean = 6.61 AV) [t(10) = 2.39, p = .038], whereas there was only a trend between change detection and no

Figure 3. Occipital P1 effect, 110 msec following the first visual display. (A) Spatial layout of the 62 electrode sites used for EEG recording. Electrodes (n = 8) selected for P1 analyses are highlighted by a shaded area on each side. A group-averaged ERP waveform (at electrode PO6) is displayed from 200 msec until +800 msec following stimulus onset, with a shaded area highlighting the time course of P1 and N170 for the first visual display (S1). (B) Axial view of P1 topography (voltage map) on the scalp (here during change detection). (C) Details of the waveform highlighted in (A), showing ERP differences (at PO6) between change detection (in gray) and change blindness (in black). The P1 was larger in the former than the latter condition, but there was no effect for the N170. (D) Mean amplitude of P1 (in OV, collapsed across the 8 selected electrodes) and standard errors (SEM ) as a function of trial type, showing enhanced P1 for correct change detection.

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change (mean = 6.77 AV) [t(10) = 2.01, p = .072]. A direct comparison between change blindness and no change did not reveal any significant effect or interaction (all p > .09). This result suggests an enhanced processing of S1 during change awareness and correct no-change trials, relative to change blindness, arising when the change has not occurred yet. For the N170 component evoked by S1, a 3 (condition) 2 (hemisphere) 2 (line) 2 (position) ANOVA on mean amplitudes did not reveal any significant effect of condition (Table 1). Thus, this face-selective component did not differ (all p > .16) between change awareness (mean = 5.9 AV), change blindness (mean = 6.1 AV), or no change (mean = 6.2 AV), as illustrated in Figure 3C. Similarly, a 3 (condition) 3 (position) ANOVA, plus follow-up pair-

wise comparisons performed on the mean amplitude of the P2 component evoked by S1 did not reveal any significant effect or interaction (all p > .22). At a later latency (during the interval between S1 and S2), a CNV-like activity was observed over fronto-central sites (Figure 4), clearly distinct from the preceding P2 and peaking approximately 290 msec poststimulus onset (see also Figure 2A). A 3 (condition) 2 (hemisphere) 3 (position) ANOVA on the mean amplitude of this frontal activity revealed a highly significant main effect of condition [F(2,20) = 4.89, p = .019]. This sustained frontal activity was systematically more negative (mean = 1.91 AV) during change blindness as compared with change detection (mean = 0.72 AV) [F(1,10) = 9.16, p = .013] and no change (mean = 1.05 AV) [F(1,10) =

Figure 4. Frontal effect, 250 msec following the first visual display. (A) Spatial layout of the 62 electrodes, with the lateral sites (n = 6) selected to analyze this frontal activity highlighted by a shaded area on each side. A group-averaged ERP waveform (electrode F7) is displayed from 200 msec until +800 msec following stimulus onset, with a shaded area highlighting the time period when a sustained frontal activity was found during change detection. (B) Axial topography (voltage map) showing an anterior negative activity during change detection, not seen during change blindness. (C) Details of the waveform highlighted in (A), showing ERP differences (at F7) between change detection (in gray) and change blindness (in black). A sustained negative shift in lateral frontal activity was systematically larger in the latter than the former condition. (D) Mean amplitude of this frontal activity (in AV, collapsed across 6 electrodes) and standard errors (SEM ) as a function of trial type, showing an enhanced negative activity for change blindness relative to both correct change detection and correct no-change report.

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4.70, p = .055]. However, there was no difference between the two latter conditions [F(1,10) = 0.74, p = .41]. This frontal effect started just before the onset of S2 (Figure 4), and was therefore not caused by any effect time-locked to this second display. Visual responses evoked by S2 showed a similar P1– N170–P2 sequence (Table 1), but with a marked attenuation of the overall amplitude of P1 (mean = 4.05 AV) and N170 (mean = 3.5 AV) as compared with the same components evoked by S1 (P1, mean = 6.89 AV; N170, mean = 6.1 AV). This reduction across the two consecutive displays was highly significant [P1, F(1,10) = 12.83, p = .005; N170, F(1,10) = 7.82, p = .019] and consistent with the well-known attenuation (‘‘gating’’) of exogenous sensory responses to stimuli presented in rapid succession (Graham, 1975). However, a 3 (condition) 2 (hemisphere) 4 (electrode) ANOVA per-

formed on the mean amplitude of the P1 evoked by S2 did not reveal any significant effect of condition or interaction with this factor (all p > .16). By contrast, the 3 (condition) 2 (hemisphere) 2 (line) 2 (position) ANOVA on the mean amplitude of the N170 evoked by S2 showed a significant effect of condition [F(2,20) = 3.61, p = .046; see Figure 5]. N170 responses to S2 were smaller when a change of face was detected (mean = 2.8 AV) as compared with change blindness (mean = 4.1 AV) [F(1,10) = 5.83, p = .036]. In the nochange condition, N170 showed an intermediate magnitude (mean = 3.4 AV; see Figure 5), not statistically different from either change detection [F(1,10) = 2.10, p = .19] or change blindness [F(1,10) = 2.01, p = .19]. Subsequent topographic analyses indicated that this amplitude difference for N170 corresponded to a distinct topography between change detection versus blindness

Figure 5. N170 effect, 150 msec following the second visual display. (A) Spatial layout of the 62 electrodes, with sites (n = 8) selected for the N170 highlighted by a shaded area on each side. A group-averaged ERP waveform (electrode PO8) is displayed from 200 msec until +800 msec following stimulus onset, with a shaded area highlighting the time course of P1 and N170 for the second visual display. (B) Axial view of N170 topography (voltage map) in the change detection condition. (C) Details of the waveform highlighted in (A), showing ERP differences (at PO8) between change detection (in gray) and change blindness (in black). The N170 was significantly smaller in the former than the latter, without any difference in P1. (D) Mean amplitude of N170 (in AV, collapsed across 8 electrodes) and standard errors (SEM ) as a function of trial type, showing a reduction of N170 during change detection as compared with change blindness, with an intermediate amplitude during no change.

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during the time range of the N170 (see below). This result suggests a modification in the visual encoding of the second face when a change is consciously perceived, as compared with blindness for the same physical change. Finally, the 3 (condition) 3 (line) 3 (position) ANOVA performed on the mean amplitude of P2 responses evoked by S2 did not reveal any significant effect of condition or interaction with this factor (all p > .16). We also analyzed the differences in a P3 component after exogenous visual responses to S2, approximately 650–750 msec posttrial onset (Table 1 and Figure 6). This revealed a highly significant effect of condition [F(2,20) = 13.71, p < .001] due to a larger P3 for change detection (mean = 9.4 AV) relative to change blindness (mean = 7.1 AV) [t(10) = 4.16, p =

.002] and no change (mean = 7.0 AV) [t(10) = 4.41, p = .001]. P3 amplitude was not different between the two latter conditions [t(10) = 0.26, p = .80]. This modulation by the detection of changes is consistent with the postperceptual P3 activity typically associated with categorization and decision processes (Polich & Kok, 1995). Topographic Modulations Changes in the topography of electric field across different conditions may arise independently of differences in strength (i.e., amplitude of the component waveforms or GFP), reflecting an activation of distinct neural generators rather than a modulation in the magnitude or latency of responses seen at discrete

Figure 6. P3 effect, 350 msec following the second visual display. (A) Spatial layout of the 62 electrodes, with sites (n = 9) selected for the P3 highlighted by a shaded area on each side. A group-averaged ERP waveform (at electrode Cz ) is displayed from 200 msec until +800 msec following stimulus onset, with a shaded area highlighting the time course of P3. (B) Axial view of P3 topography (voltage map) in the change detection condition. (C) Details of the waveform highlighted in (A), showing ERP differences (at Cz ) between change detection (in gray) and change blindness (in black). The P3 was consistently larger in the former than the latter. (D) Mean amplitude of P3 (in AV, collapsed across 9 electrodes) and standard errors (SEM ) as a function of trial type, showing an increased P3 during change detection condition relative to other conditions.

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electrode positions (Lehmann & Skrandies, 1980); although strength changes may also modify topography when using single dipole fitting (Urbach & Kutas, 2002) unlike here. Such changes in the global configuration of electrical activity over time can provide important additional information about the spatio-temporal dynamics of visual processing, not always available in conventional waveform measures (Pourtois, Dan, Grandjean, Sander, & Vuilleumier, 2005; Michel et al., 1999). To determine whether awareness versus blindness of face changes was associated with distinct topographies of neural activity (in addition to the amplitude effects described above), we applied a standardized spatial cluster analysis (or temporal segmentation method, Pascual-Marqui et al., 1995) that can define the dominant electric configuration appearing over time, across the different conditions, irrespective of changes in amplitude (see Methods). This temporal segmentation analysis identifies a sequence of statistically distinct topographic maps in the time course of EEG responses, each being presumably associated with different functional stages of information processing (i.e., microstates; see Michel et al., 1999; Lehmann & Skrandies, 1980). This approach could be particularly useful here to determine whether awareness of face changes is associated with (a) a unique functional topographic map at a specific latency (not present during blindness), (b) a prolongation of a specific map (also present but shorter during blindness), or (c) a higher strength for one or more of the maps seen in the other conditions. A temporal segmentation analysis was performed across all experimental conditions (Michel et al., 1999), using a temporal window from S1 onset until 600 msec postonset (i.e., 300 time frames including all prominent ERPs). Results revealed that the group-averaged data could be segmented by a solution of 10 different maps, explaining 94.5% of the total variance. As can be seen in Figure 7, the temporal organization of these maps was almost identical across the three conditions, with the exception of the distinctive topography of Map 4 (Figure 7D). Map 4 was not reliably elicited during change blindness (Figure 7B) relative to correct change detection (Figure 7A) and correct no-change (Figure 7C) conditions. It first appeared 220 msec after onset of S1 (i.e., 80 msec before onset of S2) and was then reproduced at several moments in both change detection and correct no-change conditions (at 350, 460, 520, and 580 msec for change detection; at 220, 350, 520 and 580 msec for correct no-change conditions, in the average group data). A careful inspection of the topographic data (see Figure 7) indicated that Map 4 associated with awareness of face change was actually elicited during time periods corresponding to low-amplitude neural events that did not overlap with the expression of ERP components (as usually revealed by higher GFP in these analyses). Thus, there was no clear relation between the P1 effect (Map 2, Figure 7D) or sustained frontal effect (Map 6, Figure 7D)

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and the expression of Map 4 (see Figure 7). This dissociation emphasizes the complementary value of component and topographic analyses. The distinctiveness of Map 4 across conditions was further tested by calculating its degree of expression (in time frames) in each individual and each condition, using a spatial fitting procedure (Michel et al., 1999), and then performing a pairwise statistical comparison between the fitting values of different conditions. This analysis confirmed that Map 4 was generally present for a shorter total duration within the 0- to 600-msec window postonset for change blindness (mean = 12.6 time frames), as compared with change detection (mean = 22.3 time frames) [t(10) = 1.76, p = .05, onetailed] or with the no-change condition (mean duration = 19.8 time frames) [t(10) = 1.82, p = .05]. There was no difference between no-change and change detection [t(10) = 0.54, p = .30]. In addition, to test for the recurring property of this map (see Figure 7A and C), we also computed the mean number of separate occurrences (>10 consecutive time frames) during the 0- to 600-msec temporal window for each subject and each condition. This confirmed that separate repetitions of this map were more frequent during change detection (mean = 2.1) than during change blindness (mean = 1.2) [t(10) = 1.76, p = .05, one-tailed]. Note that the number of separate repetitions in individual subjects does not necessarily correspond to the number of segments in the group-average results, due to the differences in duration for each occurrence in individual subjects (see Michel et al., 1999). Finally, this spatial cluster analysis also revealed that the scalp topography corresponding to the face-selective N170 component (Map 3, Figure 7D) was different between S1 and S2 during change detection (Figure 7A) but partly identical across these two successive displays during change blindness (Figure 7B) and no-change conditions (Figure 7C) [with a global duration of this topography in the 400- to 500-msec window being shorter for change detection than change blindness; t(10) = 1.81, p = .05, one-tailed]. This result adds to our finding of a significant modulation of N170 amplitude at posterior electrodes, as a function of awareness versus blindness. In other words, when a face change occurred but was missed, the topographic map corresponding to the face-selective N170 evoked by S2 partly replicated the topography evoked during S1 (Figure 7B), unlike when the face change was detected (Figure 7A). These results indicate that the local modulation of N170 at posterior electrodes was in fact associated with a concurrent change in the underlying neural generators (Lehmann & Skrandies, 1980). Some differences were also found in Map 7, subsequent to the N170 (Map 3) evoked by S2 (see Figure 7D) and concomitant with the P2 component. However, this global change in P2 topography between S1 (Map 5) and S2 (Map 7) was identical across all three conditions


Figure 7. Temporal segmentation of ERP topography. The spatial cluster analysis (from stimulus onset until 600 msec) showed a segmentation solution with 10 different maps. The succession of these maps is shown for (A) correct change detection, (B) change blindness, and (C) correct no-change reports. Time is represented along the x axis and GFP along the y axis. Each distinct topography is labeled with a number (1–10). Black and white segments indicate topographies occurring across all experimental conditions, whereas red and blue segments indicate topographies that differed between conditions. (D) Axial views of the electric field configuration (scaled to unitary strength by dividing the voltage at each electrode by the GFP) corresponding to each of the numbered map segment. A distinctive map (Map 4) is observed only during the change detection and no-change conditions, arising at several successive moments in time during the whole trial sequence (indicated by arrows). This map was not significantly expressed in the change blindness condition. Another map (Map 3) corresponding to the N170 was repeated during the second visual display (with a briefer duration than during the first display) only when no face change was perceived (i.e., during change blindness and no change), whereas it was replaced by Map 4 during change detection.

(Figure 7A–C), unlike the N170 topography that was significantly modulated by detection of changes. Source Localization Lastly, we used source localization by LAURA (Grave de Peralta Menendez et al., 2004) to estimate the likely neural generators accounting for the different topographies measured over the scalp, particularly those associated with the main peaks of ERPs (i.e., P1 and N170, respectively, Maps 2 and 3 in Figure 7D) and those that distinguished change detection from change blindness (Map 4 in Figure 7D). For the topography corresponding to P1 (Map 2 in Figure 7D), LAURA found bilateral sources in extrastriate visual cortex (Figure 8A), located medially in the inferior and superior occipital

gyrus (Brodmann’s area 18/19). This pattern is consistent with previous studies on this early occipital activity (Pourtois, Grandjean, Sander, & Vuilleumier, 2004). For the map corresponding to N170 (Map 3 in Figure 7D), LAURA identified several sources in lateral occipital and lateral temporal cortex (Brodmann’s area 19, 37), including superior temporal sulcus, compatible with faceselective activations in similar regions in previous brain imaging (e.g., Haxby, Hoffman, & Gobbini, 2000) and ERP studies (Itier & Taylor, 2004b). More importantly, for the critical recurrent map associated with change awareness (Map 4 in Figure 7D), LAURA identified a distributed network of bilateral brain sources, including the posterior parietal cortex, medial occipital lobe, and lateral inferotemporal cortex (Figure 8B). The peak of these sources was further estimated in Talairach

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Figure 8. Source localization results by LAURA. (A) Estimates of neural generators for the P1 topography (Map 2 in Figure 7D) were located in the bilateral occipital cortex, including the ventral and dorsal extrastriate areas. (B) Sources for the distinct topography associated with change awareness (Map 4 in Figure 7D) were found in a distributed network of regions in the posterior parietal and lateral occipital cortex (on both sides).

coordinates (Pascual-Marqui et al., 1994) and found to involve Brodmann’s area 7 in superior parietal lobule (±24x, 60y, 64z), as well as area 37 in posterior lateral fusiform gyrus (left: 52x, 60y, 13z; right: 46x, 67y, 6z) and area 18/19 in middle occipital gyrus (±31x, 88y, 8z). Taken together, these results indicate that a concomitant activation of parietal and more ventral temporo-occipital regions was specifically and recurrently present during trials with change awareness, as opposed to change blindness.

DISCUSSION By using EEG in a simplified flicker paradigm, our study could uncover the distinctive time course of brain activity during change blindness and change awareness, whereas visual stimuli remained identical in the two

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conditions. Critically, recording brain activity over the whole stimulus sequence, both before and subsequent to the change, allowed us to probe several stages in visual processing where change blindness might arise (Simons, 2000). Behaviorally, our participants showed few false alarms on no-change trials (9.4%), suggesting a relatively strict response criterion and, hence, a reliable perceptual awareness of visual change during correct reports. In addition, accuracy in the detection of peripheral changes was not associated with any systematic difference in performance for the central task or in horizontal eye movements, suggesting that blindness or awareness could arise despite equally good fixation in each condition. Critically, our EEG results revealed that the neural correlates of conscious change detection did not concern a single processing stage, but were distributed over a large temporal window and involved several successive


events. Strikingly, the earliest correlate of successful change detection, as compared with change blindness, arose shortly after the onset of the first visual stimulus (S1), that is, before the change itself (S2). Overall, we found a sequence of five major effects in EEG that distinguished brain activity during change awareness and change blindness. (a) During S1, correct change detection trials already differed from change blindness trials for the visual P1 (130 msec postonset), whose amplitude was larger in the former than in the latter case, although visual stimulation was identical and expectation of a change equally likely in these two conditions. The latency, scalp topography, and extrastriate sources of this early component were consistent with previous studies (Martinez et al., 1999). (b) During the interval between S1 and S2, another difference arose between change detection and blindness in a sustained negative activity over frontal regions (CNV-like potential), starting approximately 250 msec postonset, again preceding the change that occurred 50 msec later. (c) During S2, after a change of face identity, correct detections were associated with reduced amplitude of the N170, typically evoked by faces (Bentin et al., 1996). Furthermore, this modulation of N170 amplitude was concomitant with a modification of the electric field topography, suggesting some difference in the generators of N170 during awareness versus blindness of a changing face. (d) At a later postperceptual stage, 350 msec after onset of S2, conscious change detection enhanced the amplitude of the P3, in accord with an involvement in target detection and/or response selection mechanisms (Polich & Kok, 1995). (e) Finally, using a spatial cluster analysis of ERP topography over time (Pascual-Marqui et al., 1995), we could identify a specific configuration of activity (EEG Map 4; see Figure 7D) that arose during correct change detection, but not change blindness, and exhibited a recurrent time course during the stimulus sequence (during both S1 and S2), rather than a single transient occurrence. The neural sources for this unique map were estimated to involve a simultaneous activity in dorsal parietal and ventral occipito-temporal regions. Our results demonstrate for the first time that change blindness (relative to correct detection) may correspond to some failures arising during the processing of both the first and the second visual display, rather than during one of them only or than during a later comparison process only (see Simons, 2000). These findings add to previous EEG studies of change blindness that focused on correlates of change blindness and change awareness consecutive to the change (e.g., see Niedeggen et al., 2001, for a P3 effect; Koivisto & Revonsuo, 2003 for a N1 effect). Although our data are broadly consistent with these previous results and confirm that midlatency ERP components (200–300 msec) are modulated by changes in perceptual awareness (Fernandez-Duque et al., 2003; Dehaene et al., 2001; Niedeggen et al.,

2001), we show that conscious detection of a visual change does not depend on a single, discrete neural event, nor does it depend solely on processing new visual information about the change; but activity encoding the visual scene before the change may also play a crucial role ( Vogel & Machizawa, 2004). Our findings that ERPs in the change detection condition differed from those in the change blindness condition during four nonoverlapping time periods (two of them before the onset of the face change) suggest that several possible mechanisms may produce change blindness. First, it is possible that change blindness could equally result from a failure in any of these four stages (P1 for S1, CNV before S2, N170 after S2, or P3 after S2), each having an independent, but fundamental weight during conscious detection of change. Alternatively, our results are also compatible with the view that change blindness might result from a complex ‘‘syndrome’’ due to simultaneous failures of these four stages, having all a unique but complementary contribution. Finally, change blindness might be produced primarily from a defect in allocating sufficient attention resources toward the first stimulus (S1), resulting in a cascade of subsequent deficits (at the level of the subsequent CNV, N170, and P3 respectively), in agreement with previous psychophysical work (Rensink, 2002). More experimental work is needed to tease apart these different accounts of change blindness. Below, we will discuss the implications of each of these new ERP findings.

P1 and Visual Attention P1 is an early exogenous visual response generated in extrastriate cortex whose amplitude is typically enhanced by selective spatial attention through sensory gain mechanisms (Hillyard & Anllo-Vento, 1998). A larger P1 for the first visual display during change detection versus change blindness indicates that sensory processing of S1 was better in the former than the latter condition, presumably allowing then a more efficient comparison of the first pair of faces with the subsequent pair. This enhanced P1 might reflect a greater engagement of attentional resources on the initial display. However, attention did not appear to be selectively focused on the side of the upcoming change during successful detection (and vice versa, directed to the opposite side during blindness), as we did not observe a larger modulation of P1 over the hemisphere contralateral to detected changes (i.e., right occipital P1 for LVF). Instead, P1 was bilaterally enhanced, suggesting a better encoding of the entire visual display, perhaps reflecting more distributed attention or greater alertness (FernandezDuque & Posner, 1997). We surmise that this early P1 effect indicates dynamic moment-by-moment changes in attention, rather than more global fluctuations in vigilance state, because this difference was seen for P1

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during S1 only, but not for other attention-sensitive components (e.g., N1) or for P1 during S2 (i.e., a few hundreds milliseconds later). Furthermore, we analyzed only trials where the central task was correctly performed, ruling out that reduced P1 to S1 during change blindness reflected transient lapses in vigilance level, as vigilance was in fact sufficient to perform the central task during the very same trials. Moreover, correct detections were regularly interspersed among trials where changes were missed over the experiment time course, ruling out more general fatigue effects. Therefore, our results corroborate behavioral studies suggesting that attention may play a major role in the ability to detect visual changes (Rensink, 2002; Shapiro et al., 1997), with blindness being much less likely for changes in an attended object or location than for the same changes occurring outside attention (see FernandezDuque et al., 2003). But here we show that attention effects (selectively impacting on P1 activity) can arise before attention being directed to the change itself, suggesting that successful change detection critically depends upon a correct encoding of the initial display (Simons, 2000). We found no significant modulation of the P1 response to the second visual display (S2) associated with awareness of the face change, although it might be expected that correct detection of changes should also require selective attention at the time of S2. However, if we consider that attention may vary independently for S1 and S2 on a trial-by-trial basis, then it is likely that sensory processing and P1 activity for S2 could not only be enhanced on those trials when changes were detected (i.e., when sensory processing and P1 were also enhanced for the first stimulus), but could equally be enhanced on other trials when change blindness eventually ensued because processing of the first display was insufficient (and/or its maintenance during the interval). In other words, an amplification of P1 to the second display might be missed or underestimated because of the occurrence of such amplification in many trials where P1 was reduced for the first display and thus resulted in change blindness. More generally, these results support the notion that perceptual awareness is closely connected to attentive processes (Lamme, 2003; Driver & Vuilleumier, 2001). Furthermore, preparatory states in attention can modulate neural activity in visual cortex even before stimulus onset (Kastner et al., 1999), involving changes in baseline firing rates or oscillations (Tallon-Baudry et al., 2005), and in some situations, this prestimulus activity can determine or predict the subsequent perceptual processing (Tallon-Baudry et al., 2005; Austen & Enns, 2003; Super et al., 2003). In our study, the early difference in P1 for detected versus missed changes, preceding change onset, clearly demonstrates a crucial role for attentional mechanisms that can influence subsequent awareness of the changes (Rensink, 2002), although it

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did not correspond to a different content of awareness per se (Pins & Ffytche, 2003).

Sustained Frontal Activity A second neural correlate of correct change detection was found before change onset, with a slow negative activity over lateral frontal electrodes that was larger during change blindness than awareness, starting approximately 250 msec after S1 but approximately 50 msec before S2. This CNV-like activity did not differ between correct change and correct no-change reports. Therefore, as for P1, this difference cannot result from systematic anticipation or response biases in reporting changes, and it is unlikely that any anticipatory effects could have arisen more often when a change was actually going to occur and appear on the left, than in the other experimental conditions. Moreover, the size of this frontal effect was visibly as large or even larger than the P1 effect (compare Figures 4D and 3D, respectively). A similar CNV activity is often recorded during an interval (of a few seconds) between a warning or preparatory stimulus (S1) and an imperative stimulus (S2) that requires a speeded response ( Walter et al., 1964). Although here the second visual display appeared rapidly (300 msec) after the first visual display and did not strictly correspond to an imperative stimulus (the response probe screen was presented at a later point in time; see Figure 1), the present frontal effect shared many topographical similarities with the initial portion of CNV (Brunia & van Boxtel, 2001). This could index a more efficient preparation for the detection of upcoming changes (with better recruitment or focusing of attention resources) or a more efficient encoding of S1 into short-term memory. Both hypotheses might account for a more positive frontal activity during both change detection and correct no-change reports, relative to change blindness. Alternatively, it is possible that this frontal activity might relate only to the maintenance of a short-term memory trace, between the disappearance of S1 and the appearance of S2, allowing a more efficient comparison of the faces in each display. The lateral frontal topography of this activity would be consistent with a major role of frontal cortex in working-memory processes (e.g., D’Esposito, Postle, & Rypma, 2000) and converges with previous suggestions that visual attention alone is not sufficient to perceive changes in a scene (Simons & Rensink, 2005; Simons, 2000; Rensink et al., 1997). This significant frontal difference during the interval before the change indicates that successful detection may not only require the formation of a robust representation for the first display (as indexed by occipital P1 effects) but also the maintenance of this representation across time delays (presumably indexed by frontal effects).


Future ERP studies should determine which stimuli and tasks may facilitate the occurrence of these anticipatory effects (P1 and CNV) during change detection, and whether similar effects are also observed during other tasks. For example, other experimental conditions (tapping either into mechanism of perception, memory, or attention) where task difficulty is systematically manipulated might also produce partly similar patterns of ERP effects, with a differential brain activity arising before the target onset but predicting performance success at a later point in time (see Tallon-Baudry et al., 2005; Vogel & Machizawa, 2004). N170 and Awareness of Face Changes After the onset of S2, the N170 had a smaller amplitude when a change of face was detected than when it was missed or when no change occurred. This effect arose without any difference in earlier visual responses to the second display, for example, in the just preceding P1. These findings suggest that neural activity in the time range of N170, typically related to structural face processing (Bentin et al., 1996), may be sensitive to the perceived identity of faces (repeated vs. changed), but does not simply respond to the physical visual features in repeated or novel faces (Campanella et al., 2000). When missed, the same change of faces did not affect the N170, ruling out that these differences resulted from a simple visual repetition or ‘‘priming’’ effect. Moreover, the fact that the N170 did not differ between change detection and change blindness for S1, but only for S2, is consistent with an involvement of this face-specific component in some higher level mechanism of face recognition and confirms that this ERP response may differentiate changes in face identity (Campanella et al., 2000; George, Jemel, Fiori, & Renault, 1997). In keeping with this, our temporal segmentation analysis revealed that the N170 modulation for S2 did not only affect its amplitude, but also evoked a distinct topography, which was selectively modified during the correct detection of face changes relative to misses and no-change trials (i.e., with Map 4 replacing Map 3, respectively, from 150 to 170 msec after S2 onset). Thus, for both change blindness and no-change conditions, the initial N170 topography (Map 3) was repeated in part with the same configuration during the first and the second face displays (150–210 msec after S1, and 150–170 msec after S2), whereas another distinct topography followed in all conditions (Map 7, from 170 to 210 msec after S2 onset). This repetition of the same N170 activity between the first and second face displays when no change was perceived is consistent with a lack of comparison mismatch in these two conditions and suggests that such comparison (Mitroff et al., 2004; Scott-Brown et al., 2000) may partly be indexed by N170 activity (George et al., 1997). In contrast, the N170 elicited by the detection of a new face appeared to

engage a partly different network, producing a different topography distribution. Taken together, these results suggest that N170 activity may reflect high-level processes involved in face identity recognition (Itier & Taylor, 2004a; George et al., 1997), rather than structural encoding or visual categorization only (Eimer, 2000). This topography-specific N170 effect might correspond to the activation of face processing systems critically implicated in the subjective perceptual experience of a face change during correct detection trials. By providing a rapid estimate for the time when face changes are detected, these findings also converge with Koivisto and Revonsuo (2003) in that distinct neural activity around 200-msec postchange onset might correlate with differences in the content of visual awareness (see also Vuilleumier et al., 2001; Corthout, Uttl, Ziemann, Cowey, & Hallett, 1999, for a modulation of visual ERPs at similar latencies for seen vs. unseen stimuli). P300 and Change Detection After the N170, awareness of face changes produced a large and sustained positive activity with a centro-parietal topography, compatible with a classic P3 component (Polich & Kok, 1995). Similar P3 responses have already been observed during conscious detection of visual changes (Fernandez-Duque et al., 2003; Koivisto & Revonsuo, 2003; Niedeggen et al., 2001) or detection of targets with low probability (Polich & Kok, 1995; Johnson, 1986). The higher amplitude of P3 for change detection relative to the other conditions (and the lack of difference between no-change and change blindness) is consistent with a postperceptual effect, probably not reflecting changes in the content of visual awareness per se, but other aspects in the subjective experience of change, including subsequent decision and response selection stages or some form of memory updating (Polich & Kok, 1995). This late P3 effect is therefore likely to be less dependent upon specific stimulus content than the preceding N170 effect, as might be demonstrated in future experiments using different categories of visual objects. Recurrent Activity in a Distributed Network of Dorsal and Ventral Brain Areas A major novel finding in our study was provided by the temporal segmentation analysis of ERP topography, revealing that awareness of changes was associated with a recurrent neural process (corresponding to Map 4, Figure 7D) arising at several successive stages during the stimulus sequence, both before and after change onset (i.e., during both S1 and S2). This configuration of EEG activity was seen selectively during correct change detection, as well as during the correct no-change condition, although it was not reliably expressed during change blindness. This recurrent configuration of neural

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activity therefore reflected the higher success of visual awareness during correct change and correct no-change reports, in contrast to change blindness. Importantly, source estimation using LAURA (Grave de Peralta Menendez et al., 2004) suggested that this activity (Map 4) was generated by a concurrent activation of bilateral regions in superior parietal cortex and inferior temporo-occipital cortex (Figure 8B), close to the posterior and lateral fusiform regions implicated in face and object recognition (Haxby et al., 2000). These results are highly consistent with the fMRI findings of Beck et al. (2001) in a similar change blindness paradigm, where correct detection of face changes correlated with increased activation in fusiform and in posterior parietal areas, known to be involved in attentional processes (Corbetta et al., 2000). A similar pattern of ventral–temporal and dorsal–parietal activation for seen relative to unseen stimuli was found in fMRI studies of patients with spatial neglect (Vuilleumier et al., 2001, 2002), as well as during visual masking in healthy subjects (Dehaene et al., 2001). Our source localization results also converge with recent findings (Beck, Muggleton, Walsh, & Lavie, 2006) showing that repetitive transcranial magnetic stimulation over posterior parietal cortex can interfere with change detection during a flicker paradigm similar to the current task. Altogether, these data support the idea that functional coupling between distant brain areas in ventral and dorsal visual stream might play a critical role in visual awareness (Driver & Vuilleumier, 2001; Kanwisher, 2001). However, our new findings suggest that such functional coupling might not arise at a single moment in time, but involves a recurrent dynamic process, taking place during several periods over the course of stimulus processing. Such recurrent activity could not be identified in previous fMRI studies because of their poor temporal resolution as compared with EEG. Our new findings further demonstrate that a concomitant activation of parietal and visual areas may not only arise from neural events triggered by the perceived changes but may also precede the onset of changes that will be subsequently detected. Elucidating the temporal dynamics and interactions between parietal and inferotemporal areas is a central issue in current research on perceptual awareness (Block, 2005). The present results seem to challenge a simple view of awareness arising from the access of sensory information to higher level or distributed networks in a single ‘‘winner-take-all’’ step. Instead, our data support the view that awareness may result from a satedependent mechanism (Austen & Enns, 2003), with recurrent activity and cross-talk between distant areas operating in a sustained manner, through recursive loops and feedback interactions within distributed networks (Block, 2005; Di Lollo, Enns, & Rensink, 2000). Such recursive and coordinated activity might be crucial to accrue reliable information from sensory inputs and to

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establish a robust representation of visual stimuli for accurate conscious report (Di Lollo et al., 2000). Future studies are needed to determine whether similar recurrent EEG states correlate with visual awareness in other attentional or masking paradigms. No Evidence for Implicit Processing We found no ERP effect for undetected changes relative to the no-change condition, suggesting that ERPs were not sensitive to ‘‘implicit’’ processing potentially arising during change blindness (Mitroff, Simons, & Franconieri, 2002; Smilek, Eastwood, & Merikle, 2000). This is unlikely to result from our specific task (see Beck et al., 2001) or instructions emphasizing strict confidence for reporting changes (note that our subjects still made false alarms on 10% of no-change trials and less than 2% in the fMRI study of Beck et al., 2001). A conservative criterion was encouraged to ensure a reliably distinct percept during change awareness but could have contributed to include trials with a ‘‘weak’’ or ‘‘vague’’ sense of change in the change blindness condition (Rensink, 2004). Indeed, a consequence of a conservative criterion is that some proportion of the missed changes might reflect low-confidence detection. Despite this, we found no evidence that unreported changes modulated ERPs in terms of either component or topography (relative to no change). This again underscores that the four successive events in ERPs during change detection versus blindness were specifically related to differences in awareness, rather than physical inputs. However, future ERP studies should also collect confidence ratings to test if some effects might correlate with subjective certainty or qualitative aspects rather than with all-or-none detection judgments (see Sergent, Baillet, & Dehaene, 2005; Sergent & Dehaene, 2004). The lack of implicit effect in our study contrasts with behavioral evidence for implicit representation of missed changes (e.g., Fernandez-Duque & Thornton, 2000) but agrees with previous ERP studies of change blindness (Henderson, Baker, & Orbach, 2006; Koivisto & Revonsuo, 2003; Orbach & Henderson, 2003; Niedeggen et al., 2001). A late anterior effect (240–300 msec) was observed in one study (Fernandez-Duque et al., 2003) that compared ERPs to complex pictures during a continuous flicker presentation when subjects failed to see a change and when no change was present. This effect may have been amplified by the continuous (or cyclic) repetition of stimuli in this flicker paradigm. Moreover, unconscious processing might yield less synchronous electrical responses in visual pathways, precluding the recording of reliable ERPs (Driver & Vuilleumier, 2001). Conclusions Our study provides several new findings on the mechanisms of change detection and change blindness. We


show that awareness of changes may depend on distinct patterns of brain activity, implicating several successive neural events over a few hundreds milliseconds, starting already before change onset. Further, by combining classical ERPs with topographic analyses, we show that a distinct configuration of activity is associated with change awareness, characterized by concurrent activation of parietal and occipito-temporal areas at several successive latencies. Our data provide new insights on the dynamic neural underpinnings of attention and awareness.

Acknowledgments This work is supported by a grant from the Swiss National Science Foundation to P. V. (632.065935). Reprint requests should be sent to Gilles Pourtois, Neurology & Imaging of Cognition, Clinic of Neurology and Department of Neuroscience, University Medical Centre (CMU), 1 rue Michel-Servet, CH-1211 Geneva, Switzerland, or via e-mail: gilles. [email protected] or patrick.vuilleumier@medicine. unige.ch.

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